**6.7 Examples**

The following example demonstrates the results of a simulation which tracked the calorific value with historic data for 24 hours (reconstruction simulation) in a smaller city with a medium sized distribution network with some trunk lines (see figure 7).

There are six feeder points in total; in the north and the west are equal calorific values, in the south there is one point with different calorific value. (The different calorific values are made visible by different colors on the pipe segments, arrows indicate flow directions). In the middle of the network there is a mixing area (blue and pink) while near the southern feeder point the initial value dominates (dark yellow); the eastern branch of the grid shows a moderate mixed value (red). The small diagrams aside the network lines show the variable flow at the feeder points.

Gas Quality Parameter Computation in Intermeshed Networks 147

Fig. 9. Areas of influence of the feeder points (calorific value distribution distinct by colored

The next example shows the result of the tracking of the calorific value for high pressure network (see figure 10). Here two biogas plants feed into the network at different points; the three feeder points from other upstream transportation systems (not shown here) are equipped with process gas chromatographs ensuring complete information of the incoming gas qualities. The pipelines are color coded according to their calorific value. As the calorific value changes with time there are different values which are transported downstream in the pipes (visible in simulation only, not in this snapshot at a distinct time). Apart from the transport of different calorific values the areas of influence are discernable (e.g. blue colored

borders in the background: yellow/green)

pipes).

Fig. 7. Result of calorific value tracking in a distribution network; distinction by color scale (courtesy of STANET)

Figure 8 shows a detailed view of the distribution of the calorific value in the middle area of the network, figure 9 demonstrates the area of influence from the different feeder points in the same network by different colors in the background.

Fig. 8. Detailed view of calorific value distribution in the inner city area network (distinction by color)

Fig. 7. Result of calorific value tracking in a distribution network; distinction by color scale

Figure 8 shows a detailed view of the distribution of the calorific value in the middle area of the network, figure 9 demonstrates the area of influence from the different feeder points in

Fig. 8. Detailed view of calorific value distribution in the inner city area network (distinction

(courtesy of STANET)

by color)

the same network by different colors in the background.

Fig. 9. Areas of influence of the feeder points (calorific value distribution distinct by colored borders in the background: yellow/green)

The next example shows the result of the tracking of the calorific value for high pressure network (see figure 10). Here two biogas plants feed into the network at different points; the three feeder points from other upstream transportation systems (not shown here) are equipped with process gas chromatographs ensuring complete information of the incoming gas qualities. The pipelines are color coded according to their calorific value. As the calorific value changes with time there are different values which are transported downstream in the pipes (visible in simulation only, not in this snapshot at a distinct time). Apart from the transport of different calorific values the areas of influence are discernable (e.g. blue colored pipes).

Program Name

Gas Quality Parameter Computation in Intermeshed Networks 149

GANPRODA PSI AG, Berlin Integrated in SCADA;

SIMONE Liwacom, Essen Stand-alone version;

Engineering, Berlin

German Lloyd, Hamburg

Table 3. Programs for simulation and gas quality parameter tracking

STANET Fischer-Uhrig

Stoner SPS Advantica/

**7.2 IT-systems requirements** 

**8. Shortcomings, limitations** 

will work and plan with simulation results:

**8.1 Calibration aspects for network model and verification** 

values and some small scale effects on flow (e.g. sharp bendings).

Producer Features Remarks

(GANESI based);

calorific value

calorific value

parameters

calculation

The simulation programs can be executed already on a powerful PC; for big networks which have more than 100,000 pipes a powerful server type of computer with fast and large storage capacity is recommended. Computing time for static simulation ranges from seconds to few minutes (10,000 pipes about 20 s, 200,000 pipes about 150 s); dynamic

Unfortunately there are some limitations which should be known and considered when one

In order to verify the computing results the network should be calibrated first. This means in the initial phase the simulation results must be compared with and validated against measurements taken in the field, e.g. pressure at selected (better many) control points. In most cases the network points which have appropriate measurements are sparsely installed. Calibration is a project that needs an extra effort and maybe temporary installation of additional measurements. If calibration is omitted then the computed/simulation results could be uncertain at a (small) percentage in a specific area (of few measurement points). Pipeline roughness value is another value that is often not really known but estimated, only. Instead, an "integral" roughness value is used for the pipes which averages individual

simulations will take time according to the length of the period to be simulated.

Stand-alone version; online + offline simulation; tracking/reconstruction of calorific value, quality

Stand-alone network

online + offline simulation; tracking/reconstruction of

online + offline simulation; tracking/reconstruction of

Medium: gas; static + dynamic computation

Medium: gas; static + dynamic computation

Medium: gas, water, steam, electricity; static + dynamic computation

Fig. 10. Tracking oft he calorific value in a high pressure transport network and biogas feedings (A, B)
